Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network

Ahmad KhazaiePoul; M. Soleimani; S. Salahi

doi:10.1016/j.cjche.2015.11.027

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Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network

第25届中国过程控制会议专栏 | Updated：2026-01-06

- Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network
- Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network
- 中国化学工程学报（英文版） 2016年24卷第4期页码：491-498
- Affiliations：
  
  1. 2 Amol University of Special Modern Technologies, Amol 7,Iran,46168-4976
  2. 3 Chemical Engineering Department, Islamic Azad University, Shahrood Branch, Shahrood,Iran
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2015.11.027
  中图分类号：
- 纸质出版：2016
- Accepted：
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Ahmad KhazaiePoul, M. Soleimani, S. Salahi. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. 中国化学工程学报（英文版）, 2016,24(4):491-498.

Ahmad KhazaiePoul, M. Soleimani, S. Salahi. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. Chin.J.Chem.Eng., 2016, 24(4): 491-498.
Ahmad KhazaiePoul, M. Soleimani, S. Salahi. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. 中国化学工程学报（英文版）, 2016,24(4):491-498. DOI： 10.1016/j.cjche.2015.11.027.

Ahmad KhazaiePoul, M. Soleimani, S. Salahi. Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network[J]. Chin.J.Chem.Eng., 2016, 24(4): 491-498. DOI： 10.1016/j.cjche.2015.11.027.

摘要

Abstract

Nowadays artificial neural networks (ANNs) with strong ability have been applied widely for prediction of nonlinear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature

pressure

critical temperature

critical pressure

density

molecular weight and acentric factor has been used for solubility prediction of three disperse dyes in supercritical carbon dioxide (SC-CO

) and ethanol as co-solvent. Itwas shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-CO

. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposedmodel. Results showed that our proposed ANN model has correlation coefficient

Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998

0.992

and 1.053 respectively.

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